5+ Best Computer Vision Courses to know 2022 | Learn Computer Vision
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Salesforce AI Research has proposed a new video-and-language representation learning framework called ALPRO. This framework can be used for pre-training models to achieve state-of-the-art performance on tasks such as video-text retrieval and question answering.
ALPRO follows the “pre-training-then-fine-tuning” paradigm utilized in the VLP techniques described previously but overcomes their drawbacks. The approach runs on poorly sampled video frames and achieves more efficient cross-modal alignment without explicit object detectors.
The ultimate objective of the novel strategy is to enhance the performance of subsequent tasks, such as video-text retrieval and video question answering (video QA). As proposed in ALPRO, enhanced pre-training technique results in enhanced video-language representations, contributing to enhanced performance on subsequent tasks.
Continue reading | Check out the paper and github
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After 10 years and nearly 5 million enrollments, Stanford will be closing new enrollments for the Machine Learning course on Coursera from June 14, 2022. It will be replaced by a more in-depth Machine Learning Specialization by Stanford Online and Deeplearning.ai and will be available in June.
The most iconic MOOC to ever exist?
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This paper argues that the uninterpreability of deep neural networks need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs.
https://arxiv.org/abs/2206.00520
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https://youtu.be/efPrtcLdcdM
GPT-4chan was trained on over 3 years of posts from 4chan's "politically incorrect" (/pol/) board.
Website (try the model here): https://gpt-4chan.com
Model: https://huggingface.co/ykilcher/gpt-4chan
Code: https://github.com/yk/gpt-4chan-public
Dataset: https://zenodo.org/record/3606810#.YpjGgexByDU
OUTLINE:
0:00 - Intro
0:30 - Disclaimers
1:20 - Elon, Twitter, and the Seychelles
4:10 - How I trained a language model on 4chan posts
6:30 - How good is this model?
8:55 - Building a 4chan bot
11:00 - Something strange is happening
13:20 - How the bot got unmasked
15:15 - Here we go again
18:00 - Final thoughts
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Hello all,
I'm trying to get a system going for a GNN where an agent will move along a network of nodes and edges. Each state the agent has traveled to a new node, and the total distance traveled goes up. My problem is in getting the network loaded in from a networkx graph.
Heres some code that reproduces the error:
from torch_geometric.utils import from_networkx import networkx as nx nodes = [ (0, {'y': 37.3348363, 'x': -121.888113}), (1, {'y': 37.3353111, 'x': -121.887118}), (2, {'y': 37.3358288, 'x': -121.8860567}), ] edges = [ (0, 1, {'osmid': 358475012, 'oneway': False, 'highway': 'residential', 'length': 72.482, 'geometry': '', 'speed_kph': 25.0, 'bearing': 149.1}), (0, 2, {'osmid': [416909272, 680787590], 'oneway': False…
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Many applications meant for industrial equipment maintenance, trade monitoring, fleet management, and route optimization are built using open-source Cassandra APIs and drivers to process data at high speeds and low latency. Managing Cassandra tables yourself can be time consuming and expensive. Amazon Keyspaces (for Apache Cassandra) lets you set up, secure, and scale Cassandra tables […]
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Organizational diversity, equity and inclusion (DEI) initiatives are at the forefront of companies across the globe. By constructing inclusive spaces with individuals from diverse backgrounds and experiences, businesses can better represent our mutual societal needs and deliver on objectives. In the article How Diversity Can Drive Innovation, Harvard Business Review states that companies that focus […]
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Amazon SageMaker Serverless Inference is an inference option that enables you to easily deploy machine learning (ML) models for inference without having to configure or manage the underlying infrastructure. SageMaker Serverless Inference is ideal for applications with intermittent or unpredictable traffic. In this post, you’ll see how to use SageMaker Serverless Inference to reduce cost when […]
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Many companies must tackle the difficult use case of building a highly optimized recommender system. The challenge comes from processing large volumes of data to train and tune the model daily with new data and then make predictions based on user behavior during an active engagement. In this post, we show you how to use […]
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You may have applications that generate streaming data that is full of records containing customer case notes, product reviews, and social media messages, in many languages. Your task is to identify the products that people are talking about, determine if they’re expressing positive or negative sentiment, translate their comments into a common language, and create […]
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AI Weirdness: the strange side of machine learning
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Today, we’re excited to announce that Amazon SageMaker now supports the ability to configure Instance Metadata Service Version 2 (IMDSv2) for Notebook Instances, and for administrators to control the minimum version with which end-users create new Notebook Instances. You can now choose IMDSv2 only for your new and existing SageMaker Notebook Instances to take advantage […]
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Amazon Kendra offers highly accurate semantic and natural language search powered by machine learning (ML). Many organizations use GitHub as a code hosting platform for version control and to redefine collaboration of open-source software projects. A GitHub account repository might include many content types, such as files, issues, issue comments, issue comment attachments, pull requests, […]
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Financial documents such as bank, loan, or mortgage statements are often formatted to be visually appealing and easy to read for the human eye. These same features can also make automated processing challenging at times. For instance, in the following sample statement, merging rows or columns in a table helps reduce information redundancy, but it […]
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Fraud plagues many online businesses and costs them billions of dollars each year. Financial fraud, counterfeit reviews, bot attacks, account takeovers, and spam are all examples of online fraud and malicious behaviors. Although many businesses take approaches to combat online fraud, these existing approaches can have severe limitations. First, many existing methods aren’t sophisticated or […]
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This is published in Nature, so supposedly more notable than yet another multimodal experiment. But the way the article presents the results, leaves me confused about how this compares and contrasts to e.g. DeepMind Gato?
https://www.nature.com/articles/s41467-022-30761-2
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Hello everyone!! It's been a while!! Years back I released Hyperlearn https://github.com/danielhanchen/hyperlearn. It has 1.2K Github stars, where I made tonnes of algos faster.
I was a bit busy back at NVIDIA and my startup, and I've been casually developing some algos. The question is are people still interested in fast algorithms? Does anyone want to collaborate on reviving Hyperlearn? (Or making a NEW package?) Note the current package is ahhh A MESSS... I'm fixing it - sit tight!!
NEW algos for release:
PCA with 50% less memory usage with ZERO data corruption!! (Maths tricks :)) (ie no need to do X - X.mean()!!!)) How you may ask???!
Randomized PCA with 50% less memory usage (ie no need to do X - X.mean()).
Linear Regression is EVEN faster with now Pivoted Cholesky making algo …
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Some context can be provided in the prompt, but for the bigger picture it is insufficient. I understand companies will not release anything like it until they solve the bias/censorship issues somehow, but did anyone mention an internal demo, or a project in progress? Or is there a lower scale open source experimental project?
It would be so much fun to get some summaries or Q&A on the current events, latest science/tech developments, etc.
Edit (thanks u/adt): There are projects trying to connect a language model to Internet and/or some add-on memory for facts. For example, WebGPT (which might be on its way to a product launch), BlenderBot 2.0 by Meta, and Jurassic-X by AI21.
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Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models. Computers that can read and write are here, and they have the potential to fundamentally impact daily life. The future of human–machine interaction is full
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On Techno-Prophets in The Service of Humanity
Continue reading on Becoming Human: Artificial Intelligence Magazine »
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Celebrate the onset of summer this GFN Thursday with 25 more games joining the GeForce NOW library, including seven additions this week. Because why would you ever go outside? Looking to spend the summer months in Space Marine armor? Games Workshop is kicking off its Warhammer Skulls event for its sixth year, with great discounts Read article >
The post GFN Thursday Jumps Into June With 25 New Games Coming This Month appeared first on NVIDIA Blog.
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Paper: https://arxiv.org/abs/2205.15967
Website: https://sites.google.com/view/esper-paper
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I'm trying to build a simple pygame renderer following the guidelines at https://www.gymlibrary.ml/content/environment_creation/#rendering however the function Renderer is not available from gym.utils.renderer. I have installed gym version 0.23.1.
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Meta Researchers introduce a new embodied AI platform called ‘MyoSuite’ that combines motor and neural intelligence to solve biomechanical control problems using machine learning (ML). To meet the data requirements of modern machine learning (ML) algorithms, MyoSuite’s muscle models are up to 4,000 times faster than other simulators.
Since physiologically realistic movements such as twirling a pen or manipulating Baoding balls can be generated, this research could significantly impact areas such as the development of prosthetics and post-injury rehabilitation.
In the metaverse, these models will aid in creating avatars that move more realistically, making the experience more expressive and immersive.
Continue reading | Check out the paper, Github, blog and project
https://i.redd.it/srxyakklgy291.gif
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Pretraining for rapid adaptation to new games has not been explored widely on Atari games despite being a natural and well-motivated task due to its relevance to how humans transfer knowledge to new games.
Pretraining with the DT objective performs the best across all games. All methods with pretraining outperform training CQL from scratch, which verifies our hypothesis that pretraining on other games should indeed help with rapid learning of a new game.
https://i.imgur.com/lY2DH4i.png
Multi-Game Decision Transformers
https://sites.google.com/view/multi-game-transformers
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Thousands of children participate in MIT-developed artificial intelligence curriculum.
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Having an environment capable of delivering Amazon SageMaker notebook instances quickly allows data scientists and business analysts to efficiently respond to organizational needs. Data is the lifeblood of an organization, and analyzing that data efficiently provides useful insights for businesses. A common issue that organizations encounter is creating an automated pattern that enables development teams […]
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In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that […]
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Make the best trade-offs and optimise model speed with TurinTech AI
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If you’re a manager tasked with overseeing a return to the office strategy, the last year has likely been a headache for you. In the Summer of 2021, many companies had begun calling their workers back into the office, by the following Fall, the Omicron variant of Covid-19 was beginning to ramp up and, just… Read More »DSC Weekly 31 May 2020: Why Is Returning To the Office So Hard?
The post DSC Weekly 31 May 2020: Why Is Returning To the Office So Hard? appeared first on Data Science Central.
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https://arxiv.org/abs/2205.14807
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Amazon Lookout for Metrics is an AWS service that uses machine learning (ML) to automatically monitor the metrics that are most important to businesses with greater speed and accuracy. The service also makes it easier to diagnose the root cause of anomalies, such as unexpected dips in revenue, high rates of abandoned shopping carts, spikes […]
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The home-buying process can feel like an obstacle course — finding the perfect place, putting together an offer and, the biggest hurdle of all, securing a mortgage. San Francisco-based real-estate technology company Doma is helping prospective homeowners clear that hurdle more quickly with the support of AI. Its machine learning models accelerate properties through the Read article >
The post The Closer: Machine Learning Helps Banks, Buyers Finalize Real Estate Transactions appeared first on NVIDIA Blog.
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The year of the tiger comes into focus this week In the NVIDIA Studio, which welcomes 3D creature artist Massimo Righi. An award-winning 3D artist with two decades of experience in the film industry, Righi has received multiple artist-of-the-month accolades and features in top creative publications.
The post Fantastical 3D Creatures Roar to Life ‘In the NVIDIA Studio’ With Artist Massimo Righi appeared first on NVIDIA Blog.
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Identifying causal effects is an integral part of scientific inquiry. It helps us understand everything from educational outcomes to the effects of social policies to risk factors for diseases. Questions of cause-and-effect are also critical for the design and data-driven evaluation of many technological systems we build today. To help data scientists better understand and […]
The post DoWhy evolves to independent PyWhy model to help causal inference grow appeared first on Microsoft Research.
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“Clones are organisms that are exact genetic copies of any living organism. Every single bit of their DNA is identical.”
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Today, Layer goes open-source to make machine learning more accessible and contribute to ML's growth and evolution.
Machine Learning is becoming the default way to build technology. It's how you make your apps smarter, your systems more reliable and your businesses smarter. This is mostly possible by the open-science efforts; from open-source ML frameworks to open datasets.
We will open-source more including our roadmap. Meanwhile, check out our repo, and don't forget to give us a star!
https://github.com/layerai/sdk
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Last week, the 80s pop group ABBA performed a ‘hologram concert’ based on what they called as ‘ABBAtars’ By all measures in the media, it was very successful From a technological perspective, could it offer a ‘killer app’ for 5G and the Metaverse? Firstly, a hologram concert is not a hologram as we know it… Read More »Could ABBAtars be the business model for the metaverse and 5G?
The post Could ABBAtars be the business model for the metaverse and 5G? appeared first on Data Science Central.
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To reap all the benefits of cloud computing technology, it’s important to secure the cloud during and after migration.
The post How to Protect Your Cloud from Cyberattacks During and After Migration appeared first on Data Science Central.
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Ecommerce is a cutthroat industry, and it’s only getting more competitive.
The post How 3 Key Ecommerce Metrics Can Inform Your Data Analysis appeared first on Data Science Central.
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Researchers grappling with today’s grand challenges are getting traction with accelerated computing, as showcased at ISC, Europe’s annual gathering of supercomputing experts. Some are building digital twins to simulate new energy sources. Some use AI+HPC to peer deep into the human brain. Others are taking HPC to the edge with highly sensitive instruments or accelerating Read article >
The post NVIDIA Accelerates AI, Digital Twins, Quantum Computing and Edge HPC at ISC 2022 appeared first on NVIDIA Blog.
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Jorge Cardoso wears many hats, and that’s appropriate given he has so many brains. A hundred thousand of them to be exact. Cardoso is a teacher, a CTO, an entrepreneur, a founding member of the MONAI open source consortium and a researcher in AI for medical imaging. In that last role, Cardoso and his team Read article >
The post The Man With 100,000 Brains: AI’s Big Donation to Science appeared first on NVIDIA Blog.
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It’s time to start building tomorrow’s hybrid quantum computers. The motivation is compelling, the path is clear and key components for the job are available today. Quantum computing has the potential to bust through some of today’s toughest challenges, advancing everything from drug discovery to weather forecasting. In short, quantum computing will play a huge Read article >
The post The Road to the Hybrid Quantum-HPC Data Center Starts Here appeared first on NVIDIA Blog.
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As global climate change accelerates, finding and securing clean energy is a crucial challenge for many researchers, organizations and governments. The U.K.’s Atomic Energy Authority (UKAEA), through an evaluation project at the University of Manchester, has been testing the NVIDIA Omniverse simulation platform to accelerate the design and development of a full-scale fusion powerplant that Read article >
The post Scientists Building Digital Twins in NVIDIA Omniverse to Accelerate Clean Energy Research appeared first on NVIDIA Blog.
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Across Europe and the U.S., HPC developers are supercharging supercomputers with the power of Arm cores and accelerators inside NVIDIA BlueField-2 DPUs. At Los Alamos National Laboratory (LANL) that work is one part of a broad, multiyear collaboration with NVIDIA that targets 30x speedups in computational multi-physics applications. LANL researchers foresee significant performance gains using Read article >
The post HPC Researchers Seed the Future of In-Network Computing With NVIDIA BlueField DPUs appeared first on NVIDIA Blog.
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Highly accurate digital representations of physical objects or systems, or “digital twins,” will enable the next era of industrial virtualization and AI, executives from NVIDIA and BMW said Tuesday. Kicking off the ISC 2022 conference in Hamburg, Germany, NVIDIA’s Rev Lebaredian (left), vice president for Omniverse and simulation technology, was joined by Michele Melchiorre, senior Read article >
The post Hyperscale Digital Twins to Give Us “Amazing Superpowers,” NVIDIA Exec Says at ISC 2022 appeared first on NVIDIA Blog.
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The design and position of breadcrumb navigation on a webpage is typical and has become an established practice for a long time. However, as the world shifts to a mobile-first web environment, many website designers are getting it wrong or forgetting to include it in their navigation. Doing this can be a blunder because it… Read More »Best Practices for Implementing Breadcrumb SEO Strategy for Mobiles
The post Best Practices for Implementing Breadcrumb SEO Strategy for Mobiles appeared first on Data Science Central.
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https://www.kaggle.com/competitions/amex-default-prediction/overview
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AI Weirdness: the strange side of machine learning
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A nice video by MKBHD that gives some really nice insights on the effect that Dalle 2 or ml will have in the future.
Considering that the model can make multiple examples in a very short space of time, things are getting very interesting and scary l will say.
https://youtu.be/MwAAH9tBoMg
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Article and a source code:
https://dmitryelj.medium.com/solving-sudoku-in-real-time-using-a-convolutional-neural-network-and-opencv-e47a92478dce
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I have a question about SimCLR that I have not been able to understand.
In the numerator of the SimCLR loss function, $z_i$ is the original image, and $z_j$ is the augmented version of $z_i$. We want the distance to those to be small.
Similarly in the denominator, $z_k$ for K = 1:2N, k =/= i, is the index of all other images in the batch. Those are going to be pushed away from $z_i$.
This is fine, but what guarantee do we have that k wont belong to an image of the same class as image i?
The way this is structured, we will also end up pushing away images of the same class.
Thanks
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In deep learning, there are different training methods. Which one we use in an AI project depends on the data provided by our customer: how…
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Can machines experience emotions? They might, according to Hume AI, an AI research lab and technology company that aims to “ensure artificial intelligence is built to serve human goals and emotional well-being.” So how can AI genuinely understand how we are feeling, and respond appropriately? On this episode of NVIDIA’s AI Podcast, host Noah Kravitz Read article >
The post A Devotion to Emotion: Hume AI’s Alan Cowen on the Intersection of AI and Empathy appeared first on NVIDIA Blog.
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It’s a beautiful day to play video games. And it’s GFN Thursday, which means we’ve got those games. Ten total titles join the GeForce NOW library of over 1,300 games, starting with the release of Roller Champions – a speedy, free-to-play roller skating title launching with competitive season 0. Rollin’ Into the Weekend Roll with Read article >
The post Ready, Set, Game: GFN Thursday Brings 10 New Titles to GeForce NOW appeared first on NVIDIA Blog.
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Recalling the French linguist who deciphered the Rosetta Stone 150 years ago, Hewlett Packard Enterprise today switched on a tool to unravel its customers’ knottiest problems. The Champollion AI supercomputer takes its name from Jean-François Champollion (1790-1832), who decoded hieroglyphics that opened a door to study of ancient Egypt’s culture. Like Champollion, the mega-system resides Read article >
The post Deciphering the Future: HPE Switches on AI Supercomputer in France appeared first on NVIDIA Blog.
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Today, Packaging is not just about covering a product for a better sale. Interactive packaging is a new trend in the packaging industry which mainly focuses on customer satisfaction and engagement. BLE codes, AI, and IoT are key technologies in interactive packaging which is helping users with the product attributes and user instructions. Also, Incorporation… Read More »Interactive Packaging: How to Make Packaging Smarter with AI and IoT
The post Interactive Packaging: How to Make Packaging Smarter with AI and IoT appeared first on Data Science Central.
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First Medium article is out!
Come see how Optumi is thinking about the shifting workflow needs of data science and machine learning professionals.
https://medium.com/@optumi/scale-ml-experiments-from-jupyterlab-to-the-cloud-141bd645d8e9
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If you are interested in getting your text converted to an image by Google Brain Imagen use the following link:
https://twitter.com/mo_norouzi/status/1529497457234780162?s=20&t=3K_M972bMeGRR2wG6kobHQ
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This blog post (Optimizing TensorFlow Lite Runtime Memory) says that TensorFlow Lite employs different approaches to handle intermediate tensors which occupy large amounts of memory. Is one of them DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training method?
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https://github.com/visualdatabase/fastdup
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A new training approach yields artificial intelligence that adapts to diverse play-styles in a cooperative game, in what could be a win for human-AI teaming.
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Modeling study suggests that the muffled environment in utero primes the brain’s ability to interpret some types of sound.
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Hi, all,
Glad to share an open source repository PaddleSpeech, which provides SOTA/Streaming ASR witch punctuation, influential TTS with text frontend and a product-ready VPR System.
Code:https://github.com/PaddlePaddle/PaddleSpeech
Features Set:
📦 Ease of Use: low barriers to install. The CLIs are available to quick-start your project.
🔬 Align to the State-of-the-Art: provide high-speed and ultra-lightweight models, and also cutting-edge technology.
🏆 Streaming ASR and TTS System: provide production ready streaming asr and streaming tts system.
💯 Rule-based frontend: the frontend contains Text Normalization and Grapheme-to-Phoneme (G2P, including Polyphone and Tone Sandhi).
🛎️ Multi-language: both English and Chinese are supported.
Examples:
Speech Recognition
Input wav: Input.wav
Output text: I knocked at the door on the ancient side of the building.
Text-to-Speech
Input text: Life was like a box of chocolates, you never know what you're gonna get.
Output wav: Output.wav
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https://imagen.research.google/
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TL;DR
We made autoregressive transformer based models like T5-large 2X faster than 🤗 Hugging Face Pytorch with 3 simple tricks:
storing 2 computation graphs in a single Onnx file 👯: this let us have both cache and no cache support without having any duplicated weights. When cache is used, attention switch from quadratic to linear complexity (less GPU computation) and Onnx Runtime brings us kernel fusion (less memory bound ops);
zero copy 💥 to retrieve output from Onnx Runtime: we leverage Cupy API to access Onnx Runtime internal CUDA arrays and expose them through Dlpack to Pytorch. It may sound a bit complex, but it let us avoid output tensors copy which limit our memory footprint and make us much faster (check notebook for other benefits of this approach);
a generic tool to conv…
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In the simple spread env, only position is tracked (https://github.com/openai/multiagent-particle-envs/blob/47e9ee38e605f8a563370b3c7e52a349eca3f6b1/multiagent/scenarios/simple_spread.py#L40)
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Hey guys!
I'm a student and I'm currently working on my dissertation for University. I'm using this as a way of collecting data on the representation of AI in movies and pop culture and I'd appreciate the responses!
Here's the link:
https://forms.gle/1jrzrfuSd3rFD6A17
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Codex is now powering 70 different applications across a variety of use cases through the OpenAI API.
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Digital twins that revolutionize the way the most complex products are produced. Silicon and software that transforms data centers into AI factories. Gaming advances that bring the world’s most popular games to life. Taiwan has become the engine that brings the latest innovations to the world. So it only makes sense that NVIDIA leaders brought Read article >
The post NVIDIA Brings Data Center, Robotics, Gaming, Content Creation Innovations to COMPUTEX appeared first on NVIDIA Blog.
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In the worldwide effort to halt climate change, Zac Smith is part of a growing movement to build data centers that deliver both high performance and energy efficiency. He’s head of edge infrastructure at Equinix, a global service provider that manages more than 240 data centers and is committed to becoming the first in its Read article >
The post NVIDIA Adds Liquid-Cooled GPUs for Sustainable, Efficient Computing appeared first on NVIDIA Blog.
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More than 30 leading technology partners worldwide announced this week the first wave of NVIDIA Jetson AGX Orin-powered production systems at COMPUTEX in Taipei. New products are coming from a dozen Taiwan-based camera, sensor and hardware providers for use in edge AI, AIoT, robotics and embedded applications. Available worldwide since GTC in March, the NVIDIA Read article >
The post NVIDIA Partners Announce Wave of New Jetson AGX Orin Servers and Appliances at COMPUTEX appeared first on NVIDIA Blog.
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The future of content creation was on full display during the virtual NVIDIA keynote at COMPUTEX 2022, as the NVIDIA Studio platform expands with new Studio laptops and RTX-powered AI apps — all backed by the May Studio Driver released today.
The post Master of Arts: NVIDIA RTX GPUs Accelerate Creative Ecosystems, Delivering Unmatched AI and Ray-Tracing Performance appeared first on NVIDIA Blog.
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Delegation meets campus leaders, with an eye toward AI applications and the Icelandic language.
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In recent years, a significant part of neuroscience research has focused on relating deep learning architectures to the human brain, and many deep learning (DL) techniques have recently been shown to replicate neural firing patterns observed in the brain. For example, representations of convolutional neural networks have been shown to predict neurons in the visual cortex and inferior temporal cortex, while recurrent neural networks have been shown to recapitulate grid cells in the medial entorhinal cortex. The ability to use machine learning models to predict brain representations allows for a deeper understanding of the mechanistic computations of the respective brain areas and a deeper understanding of the nature of the models. However, one of the most exciting and promising new architec…
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The StatQuest Illustrated Guide To Machine Learning
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Imagen - unprecedented photorealism × deep level of language understanding
Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Human raters prefer Imagen over other models (such as DALL-E 2) in side-by-side comparisons, both in terms of sample quality and image-text alignment.
https://gweb-research-imagen.appspot.com/
https://gweb-research-imagen.appspot.com/paper.pdf
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Hi!
As a Machine Learning Engineer, I was studying the design patterns behind scikit-learn's API (you can see here and here) and I was wondering if any of you know of something similar but for R that I can check.
Note: I am asking about R because that's what I am using and it is difficult to find something functional programming oriented for other languages, but any other library you find interesting is welcome!
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Nearly every forward-thinking organization uses analytics in recruitment to bring efficiency to its hiring process. A significant…
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It’s a well-known challenge that large language models (LLMs)—growing in popularity thanks to their adaptability across a variety of applications—carry risks. Because they’re trained on large amounts of data from across the internet, they’re capable of generating inappropriate and harmful language based on similar language encountered during training. Content moderation tools can be deployed to […]
The post (De)ToxiGen: Leveraging large language models to build more robust hate speech detection tools appeared first on Microsoft Research.
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Advances in platform models—large-scale models that can serve as foundations across applications—have significantly improved the ability of computers to process natural language. But natural language processing (NLP) models are still far from perfect, sometimes failing in embarrassing ways, like translating “Eu não recomendo este prato” (I don’t recommend this dish) in Portuguese to “I highly […]
The post Partnering people with large language models to find and fix bugs in NLP systems appeared first on Microsoft Research.
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Electric utilities are taking a course in machine learning to create smarter grids for tough challenges ahead. The winter 2021 megastorm in Texas left millions without power. Grid failures the past two summers sparked devastating wildfires amid California’s record drought. “Extreme weather events of 2021 highlighted the risks climate change is introducing, and the importance Read article >
The post Energy Grids Plug into AI for a Brighter, Cleaner Future appeared first on NVIDIA Blog.
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If someone is curious, I updated the benchmarks after the PyTorch team fixed the memory leak in the latest nightly release May 21->22. The results are quite improved:
https://preview.redd.it/5dkat9hoi3191.png?width=2637&format=png&auto=webp&s=dc42ee03167dd3aefbd0319061994bfc2ff24dab
For a more detailed write-up please see https://sebastianraschka.com/blog/2022/pytorch-m1-gpu.html
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I am curious to understand the reason behind the decision to use Skip-gram rather than CBOW for these two models. According to the original Word2vec paper, CBOW is faster to train and captures syntactic similarities better whereas the skip-gram is slower at training but captures more robust semantic similarities and is also better at handling infrequent words. How does this apply to graph theory and what motivated this decision?
DeepWalk: https://dl.acm.org/doi/abs/10.1145/2623330.2623732
Node2vec: https://dl.acm.org/doi/abs/10.1145/2939672.2939754
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I am pleased to share the news with the ML community that the team I work for recently released a new benchmark: MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions.
Our paper was accepted at Conference on Health, Inference, and Learning (CHIL) 2022 and published in Proceedings of Machine Learning Research (PMLR).
MedMCQA sample questions
The main contributions:
MedMCQA has More than 194k high-quality Medical entrance exam MCQs.
Dataset requires a deeper domain and language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects & topics.
It Covers 2.4k healthcare topics and 21 medical subjects with an average token length of 12.77, the …
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By 2050, AI will reach remarkable advancements that will be beyond many people's wildest dreams. Robots will not only be able to attain, but also generate, that task in a cost-effective, timely, and meticulous manner, hence increasing efficiency. Read more
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My Dear AI Fellows,
Please check out my latest video about how to control an AGI via Motivation Selection:
https://youtu.be/rLB4xkwgEAw
I also have a lot of great content on the channel regarding life 3.0, building an AGI, AGI Safety, etc. Please check them out and subscribe to my channel!
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Partly inspired by this article: https://www.quantamagazine.org/machine-scientists-distill-the-laws-of-physics-from-raw-data-20220510/, which describes AI that discovers new biology/physics equations from raw data.
My question is: humans have come a long way from throwing stones to having all the technologies today. This thousands of years of evolution is a process in which new knowledge is developed from existing knowledge–countless cycles of observation, experimentation, and conclusion. Is it possible then, to train an AI to capture this process of generating new knowledge from existing knowledge, and use this AI to fast-forward scientific evolution, thus quickly obtaining future technology that would otherwise take decades to develop?
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https://www.youtube.com/watch?v=0kEqqP8PlUw
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Interested in doing research in neurobiologically-inspired artificial neural networks? Need an open-source, actively maintained tool for reproducing the latest paper on predictive coding or building your own more biologically-faithful neural system? ngc-learn is a recently-released Python library designed in response to these questions.
The ngc-learn dynamics simulator is specifically meant for building, simulating, and analyzing arbitrary predictive coding models based on the neural generative coding (NGC) computational framework and theoretically guided by the free energy principle. This toolkit, distributed under the 3-Clause BSD license, is built on top of Tensorflow 2. Notably, ngc-learn's extensible nodes-and-cables system is general and can even be used to build non-predictive cod…
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Hey Reddit! A heated debate is going on today on the state of the strategic race between the United States and China to dominate in AI. I decided to gather some facts and analyzed publications at ICML 2021 and NeurIPS 2021. Here are the findings -- would love to hear what you think! 🤝❤️🤖
https://thundermark.medium.com/ai-research-rankings-2022-sputnik-moment-for-china-64b693386a4
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Forgive me for sounding irritated, but is there absolutely nothing I can use right now, be it a website, program, mobile app, whatever, that I can just pump some cool stuff out of? Why are we all being shown this amazing technology only to be told "Yo this sick tech exists, but you ain't fuckin using it lol" except for a few select people (e.g. MKBHD's access to dalle 2 for a day)..
I'm not talking about excuses such as nightcafe or wombodream.. I've used them to death and had nothing but quite frankly terrible results and want something that I can pop in terms such as, I don't know, "Drift car nissan silvia" that gives a picture of an actual car for album art etc, or something along those lines, if not just to admire how crazy AI is becoming.
Look, if this stuff existed but was kept completely secret, then the whole 'what you don't know can't hurt you' idea would apply and I would not be pissed off. What is frustrating is that all this wizardry is being flaunted and dangled in our faces whilst also being kept out of reach.
So my initial question still applies, is there anything remotely available right now that can generate images somewhat comparible?
Thanks!
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Terraform Provider Iterative (TPI) is first technological product that simplifies ML training on any cloud as it helps the infrastructure and ML team members save significant time and money in maintaining and configuring the training infrastructure: Iterative adoption of an open-source tool that is the first to train machine learning models on any cloud using Terraform
Terraform provides a flexible CLI service system for managing hundreds of cloud services, and TPI enables data scientists to delegate responsibilities without discovering software.
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The 5th episode of the webinar series on Automated CV Pipelines is coming up! It will be covering automatic instance segmentation and methods to streamline the annotation process.
If you're interested, you can register here!
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There are multiple reasons companies are finding investment opportunities in AI that are going to be beneficial for them in the year 2021. Investment in AI will help a wide range of organizations go through the economic crisis as they emerge from the pandemic. Read more
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With the fast-growing and high-density global air traffic, ensuring efficiency and air transportation safety becomes a critical challenge. AI is already revolutionizing the way air traffic management systems are manufactured and hence is believed to play a key role in optimizing air traffic flow. Read more
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AI advancements in digital technology are growing, and today we have far more technical capability than we had in the 90s, with the potential to expand even quicker in the future. Is, however, the continuance of AI growth in the best interests of humanity? Read more
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Artificial Intelligence is one of the most powerful things humans have been working on for decades, and its limitless magical spells are altering our lives. Read more
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Building next-generation intelligent vehicles requires an AI infrastructure that pushes the cutting edge. Electric vehicle maker NIO is using NVIDIA HGX to build a comprehensive data center infrastructure for developing AI-powered, software-defined vehicles. With high-performance compute, the automaker can continuously iterate on sophisticated deep learning models, creating robust autonomous driving algorithms in a closed-loop environment. Read article >
The post From Cloud to Car: How NIO Develops Intelligent Vehicles on NVIDIA HGX appeared first on NVIDIA Blog.
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Continue reading on Becoming Human: Artificial Intelligence Magazine »
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Study shows AI can identify self-reported race from medical images that contain no indications of race detectable by human experts.
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I would like to create texts with which I can then also shoot a TikTok: this kind of tiktok https://www.youtube.com/watch?v=QEmL-zPBiKs&t=11s: and I don't want to search for texts but create them with an AI right away.
Is it possible?
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https://link.springer.com/article/10.1007/s11948-022-00378-1
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AI systems, like Jasper AI, can write essays on any topic, just with one click- you don’t need to be an expert in writing and stay up late…
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In recent years, social media has become a common means for sharing and consuming news. However, the spread of misinformation and fake news on these platforms has posed a major challenge to the well-being of individuals and societies. Therefore, it is imperative that we develop robust and automated solutions for early detection of fake news […]
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FORMULA 1 (F1) cars are the fastest regulated road-course racing vehicles in the world. Although these open-wheel automobiles are only 20–30 kilometers (or 12–18 miles) per-hour faster than top-of-the-line sports cars, they can speed around corners up to five times as fast due to the powerful aerodynamic downforce they create. Downforce is the vertical force […]
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Since the global financial crisis, risk management has taken a major role in shaping decision-making for banks, including predicting loan status for potential customers. This is often a data-intensive exercise that requires machine learning (ML). However, not all organizations have the data science resources and expertise to build a risk management ML workflow. Amazon SageMaker […]
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Fortnite on GeForce NOW with touch controls on mobile is now available to all members, streaming through the Safari web browser on iOS and the GeForce NOW Android app. The full launch — including the removal of the waitlist — follows a successful beta period in which more than 500,000 participants streamed over 4 million Read article >
The post ‘Fortnite’ Arrives This GFN Thursday With GeForce Performance You Can Touch appeared first on NVIDIA Blog.
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Data management agility has become of key importance to organizations as the amount and complexity of data continues to increase, along with the desire to avoid creating new data silos. The concept of creating a ‘data fabric’ as an agile design concept has been proposed by leading analysts, such as Mark Beyer, Distinguished VP Analyst… Read More »The Foundation of Data Fabrics and AI: Semantic Knowledge Graphs
The post The Foundation of Data Fabrics and AI: Semantic Knowledge Graphs appeared first on Data Science Central.
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Since its inception, algorithmic trading has been a popular strategy for investors. It uses mathematical rules to automate the trading of various assets, such as stocks and futures. However, it has been very challenging for people who don’t have the necessary skills and knowledge — as reported by Psychology Today. According to Nasdaq, one of… Read More »How to Use the Resources of MQL5.community to Empower Your Own Business
The post How to Use the Resources of MQL5.community to Empower Your Own Business appeared first on Data Science Central.
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Keras has launched a computer vision extension package.
Links:
- https://keras.io/keras_cv/
- https://github.com/keras-team/keras-cv/
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https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
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This paper https://paperswithcode.com/paper/handcrafted-localized-phase-features-for claims to achieve 98% top-1 accuracy on kinetics-400 and 96.35 on kinetics-700. From their description, they compute phase-correlation on large patches between consecutive frames and then use that in a knn-classifier. I didn't find any extra info in the paper about the method and frankly I find it hard to believe this beats all of the recent state-of-the art methods.
What do you think? Maybe a (possibly uninteded) foul in the evaluation method?
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According to an article published in Bloomberg,
An Apple Inc. executive who left over the company’s stringent return-to-office policy is joining Alphabet Inc.’s DeepMind unit, according to people with knowledge of the matter.
Ian Goodfellow, who oversaw machine learning and artificial intelligence at Apple, left the iPhone maker in recent weeks, citing the lack of flexibility in its work policies. The company had been planning to require corporate employees to work from the office on Mondays, Tuesdays and Thursdays, starting this month. That deadline was put on hold Tuesday, though.
https://www.bloomberg.com/news/articles/2022-05-17/ian-goodfellow-former-apple-director-of-machine-learning-to-join-deepmind
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I am a student currently studying Software Development at MCAST. For my degree thesis, I am exploring the potential of MIDI sequence generation using Machine Learning techniques.
To evaluate the implemented algorithm, I created a questionnaire which asks respondents to rate 10 different samples.
No personally identifiable information will be collected in this questionnaire.
I would greatly appreciate if you can spare around 5 minutes to take part in this questionnaire.
https://www.survio.com/survey/d/Y1W7D8P1X3J7F8U7Y
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Wow, my blog “Is Data Mesh Fool’s Gold? Creating a Business-centric Data Strategy” created quite a stir. And that was my intention. I actually believe that the Data Mesh is an important data management and governance framework (yes, the Data Mesh is more of a framework than a technology) for helping organizations deliver a business-driven… Read More »Part 2: Is Data Mesh Fool’s Gold? Not if You Avoid the Traps
The post Part 2: Is Data Mesh Fool’s Gold? Not if You Avoid the Traps appeared first on Data Science Central.
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We are happy to invite you to the Gradio Blocks Party - a community event in which we will create interactive demos for state-of-the-art machine learning models. Demos are powerful because they allow anyone — not just ML engineers — to try out models in the browser, give feedback on predictions, identify trustworthy models. The event will take place from May 17th to 31st. We will be organizing this event on Huggingface: https://huggingface.co/Gradio-Blocks and the Hugging Face discord channel. Prizes will be given at the end of the event, see the Prizes section
We will be building demos using the new Gradio Blocks API. Blocks allows you to build web-based demos in a flexible way using the Gradio library. Gradio is a popular choice for building demos for machine learning models, as it allows you to create web-based UIs all in Python. For example, here is a UI for Dall-E Mini using Gradio Blocks:
https://reddit.com/link/ury6a9/video/p8m2arag24091/player
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https://preview.redd.it/65gejwx5q2091.png?width=1000&format=png&auto=webp&s=107c54464a27b005bf139eabd405134dafe94d15
More like this at: https://www.evilaicartoons.com/
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MIT and Mass General Brigham researchers and physicians connect in person to bring AI into mainstream health care.
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Researchers use artificial intelligence to help autonomous vehicles avoid idling at red lights.
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Real-time rendering is helping one studio take virtual production to impossible heights. In their latest project, the creators at Los Angeles-based company Impossible Objects were tasked with depicting an epic battle between characters from the upcoming video game, Diablo Immortal. But the showdown had to take place on the surface of a Google Pixel phone, Read article >
The post Mission Made Possible: Real-Time Rendering Helps Studio Create Cinematic Battle Between Characters From ‘Diablo Immortal’ appeared first on NVIDIA Blog.
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Eyal Ben-Ari just took his first shot on a goal of bringing professional-class analytics to amateur soccer players. The CEO of startup Track160, in Tel Aviv, has seen his company’s AI-powered sports analytics software tested and used in the big leagues. Now he’s turning his attention to underserved amateurs in the clubs and community teams Read article >
The post AI on the Ball: Startup Shoots Computer Vision to the Soccer Pitch appeared first on NVIDIA Blog.
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Concept artist Pablo Muñoz Gómez dives In the NVIDIA Studio this week, showcasing artwork that depicts a fantastical myth. Gómez, a creator based in Australia, is equally passionate about helping digital artists, teaching 3D classes and running the Zbrush guides website with his creative specialties: concept and character artistry.
The post Concept Artist Pablo Muñoz Gómez Enlivens Fantasy Creatures ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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Amazon Polly is a text-to-speech service that uses advanced deep learning technologies to synthesize natural-sounding human speech. It is used in a variety of use cases, such as contact center systems, delivering conversational user experiences with human-like voices for automated real-time status check, automated account and billing inquiries, and by news agencies like The Washington […]
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PS: This entire article was written by an AI story generator: Jasper AI.
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Most folks think Artificial Intelligence (AI) is a novel notion, although it's been around for a long time. We went back in history and curated a list of all key artificial intelligence breakthroughs that have enabled us to live our current lives. Read more
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CPUs were not as powerful and efficient a few decades ago when it came to running large computations for machine learning. Hardware manufacturers have worked hard to create a processing unit capable of performing any AI operation. Read more
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The release of Generative Pretrained Transformer (GPT-2) has fetched huge attention towards generative language models (LMs), which are pre-trained on massive amounts of unstructured text and have generated efficient results on a variety of NLP applications. LMs can produce texts constantly utilizing a textual prompt’s next-token prediction decoding approach. Models such as CLIP and ALIGN, pre-trained image-text joint embedding approaches, have revived multimodal illustration learning of text and images. Accordingly, it is challenging to integrate the benefits of pre-trained LMs and image-text embedding models to generate visually grounded text. The traditional approaches are generally limited by the object detectors trained with a fixed set of labels. Currently, the ZeroCap approach is ut…
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A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when […]
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Federated learning has become a major area of machine learning (ML) research in recent years due to its versatility in training complex models over massive amounts of data without the need to share that data with a centralized entity. However, despite this flexibility and the amount of research already conducted, it’s difficult to implement due […]
The post FLUTE: A scalable federated learning simulation platform appeared first on Microsoft Research.
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Hi All,
We have released a new version of python library, VevestaX. The library does automatic EDA and experiment tracking in a spreadsheet.
The library can be downloaded using:
pip install vevestaX
Following is the link to its demo:
https://youtu.be/7jmnIOqBpJM
Following is the github link:
https://github.com/Vevesta/VevestaX/blob/main/README.md
Following is the sample output spreadsheet:
https://docs.google.com/spreadsheets/d/15lOXzpcUQtkYQAEnx-YTegvg8zCW6pEK/edit?usp=sharing&ouid=103382336064969333270&rtpof=true&sd=true
Please give us a github star, it would mean the world for us.
Please mail your feature requests to OP at vevestax@vevesta.com
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Machine learning has long aimed to provide models with intelligence comparable to humans. Humans can automatically blend multiple sensory inputs like visual, linguistic, and acoustic signals to generate a complete knowledge of their surroundings by virtue of their intelligence. Even the most robust pre-trained AI models, in contrast to humans, are incapable of doing so, confining themselves to one or two input modalities. Researchers have always been interested in developing effective multimodal learning strategies to support this viewpoint. In their new paper, to further support this idea, the Microsoft Azure Cognitive Services Research team proposes a self-supervised pretraining framework names i-Code: An Integrative and Composable Multimodal Learning Framework.
Continue Reading
Paper: https://arxiv.org/pdf/2205.01818.pdf
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Pre-training of models for NLP applications, exclusively focus on text-level manipulation, while neglecting layout and style information that is vital for document image understanding.
This paper proposes LayoutLM that jointly model interactions between text and layout information across scanned document images. Fits very well in use-cases like Resume parsing, Bills parsing, Table parsing, etc.
Per Summary: https://youtu.be/ewyDVIdKXm0 Paper Link: https://arxiv.org/abs/1912.13318
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We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up […]
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In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). In March 2022, we also announced the support for APIs in JumpStart. JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across […]
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I wrote a post arguing that video games are more relevant than ever for AI research. Essentially, RL is at an impasse, and all the really impressive progress comes from self-supervised learning. Could we learn behavior foundation models from millions of traces of real humans playing real games, and would this get us more general and more real intelligence?
https://modl.ai/learning-ai-from-players/
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https://reddit.com/link/uosqgm/video/pxk7h4jb49z81/player
You can try it out in Colab here: https://colab.research.google.com/drive/1E5oU6TjH6OocmvEfU-foJfvCTbTfQrqd?usp=sharing#scrollTo=cVxS_6rBmLKW
To install:
pip install thousandwords
Then in Jupyter Notebook:
from thousandwords import share
Then:
%%share # Your Python code goes here..
More details: https://docs.1000words-hq.com/docs/python-sdk/share
Source: https://github.com/edouard-g/thousandwords
Homepage: https://1000words-hq.com
-------------------------------
EDIT:
Thanks for upvotes and the feedback.
People have voiced their concerns of inadvertent data leaks, and that the Python package wasn't doing enough to warn the user ahead of time.
As a short-term mitigation, I've pushed an update. The %%share magic now warns the user about exactly what gets shared and requires manual confirmation (details below).
We'll be looking into building an option to share privately.
Feel free to ping me for questions/concerns.
More details on the mitigation:
from thousandwords import share x = 1
Then:
In [3]: %%share ...: print(x) This will upload 'x' server-side. Anyone with the link will have read access. Do you wish to proceed ? [y/N]
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When it comes to safety, efficiency and sustainability, autonomous vehicles are delivering a clean sweep. Autonomous vehicle company and NVIDIA Inception member WeRide this month began a public road pilot of its Robo Street Sweepers. The vehicles, designed to perform round-the-clock cleaning services, are built on the high-performance, energy-efficient compute of NVIDIA. The fleet of Read article >
The post Broom, Broom: WeRide Revs Up Self-Driving Street Sweepers Powered by NVIDIA appeared first on NVIDIA Blog.
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https://github.com/microsoft/Swin-Transformer
The ImageNet-22K pretrained Swin-V1-Tiny and Swin-V1-Small models are also released
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nebullvm is an opensource library that generates an optimize version of your deep learning model that runs 2-10 times faster in inference without performance loss by leveraging multiple deep learning compilers (openvino, tensorrt, etc.). And thanks to today's new release, nebullvm can accelerate up to 30x if you specify that you are willing to trade off a self-defined amount of accuracy/precision to get even lower response time and a lighter model. This additional acceleration is achieved by exploiting optimization techniques that slightly modify the model graph to make it lighter, such as quantization, half precision, distillation, sparsity, etc.
The goal of nebullvm is to help other developers benefit from the most advanced inference optimization techniques without having to spend countless hours understanding, installing, testing and debugging these powerful technologies.
Hoping you enjoy the project, and please give feedback if you have any. You can also find more information (benchmarks, tutorials, notebooks) on github! And happy acceleration :)
https://github.com/nebuly-ai/nebullvm
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In the last decade, computer vision use cases have been a growing trend, especially in industries like insurance, automotive, ecommerce, energy, retail, manufacturing, and others. Customers are building computer vision machine learning (ML) models to bring operational efficiencies and automation to their processes. Such models help automate the classification of images or detection of objects […]
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Organizations use agile project management platforms such as Atlassian Jira to enable teams to collaborate to plan, track, and ship deliverables. Jira captures organizational knowledge about the workings of the deliverables in the issues and comments logged during project implementation. However, making this knowledge easily and securely available to users is challenging due to it […]
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This blog post is co-written by Jonathan Lee, Nelson Leung, Paul Min, and Troy Squillaci from Intel. In Part 1 of this post, we discussed how Intel®3DAT collaborated with AWS Machine Learning Professional Services (MLPS) to build a scalable AI SaaS application. 3DAT uses computer vision and AI to recognize, track, and analyze over 1,000 […]
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Many companies are overwhelmed by the abundant volume of documents they have to process, organize, and classify to serve their customers better. Examples of such can be loan applications, tax filing, and billing. Such documents are more commonly received in image formats and are mostly multi-paged and in low-quality format. To be more competitive and […]
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Bengal tigers, red pandas and mountain gorillas are among the world’s most familiar endangered species, but tens of thousands of others — like the Karpathos frog, the Perote deer mouse or the Mekong giant catfish — are largely unknown. Typically perceived as lacking star quality, these species are now roaming massive billboards in one of Read article >
The post Urban Jungle: AI-Generated Endangered Species Mix With Times Square’s Nightlife appeared first on NVIDIA Blog.
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Good. Bad. You’re the Guy With the Gun this GFN Thursday. Get ready for some horrifyingly good fun with Evil Dead: The Game streaming on GeForce NOW tomorrow at release. It’s the 1,300th game to join GeForce NOW, joining on Friday the 13th. And it’s part of eight total games joining the GeForce NOW library Read article >
The post GFN Thursday Gets Groovy As ‘Evil Dead: The Game’ Marks 1,300 Games on GeForce NOW appeared first on NVIDIA Blog.
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AI Weirdness: the strange side of machine learning
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Your new Machine Learning project is about to fail. Yes, you read that right.
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Researchers devise an efficient protocol to keep a user’s private information secure when algorithms use it to recommend products, songs, or shows.
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🙌 Hey all, check out our work on AiMLflow!
We are building a tool that mounts the MLflow logs and enables an Aim-based super-performant UI for metric, image and other ML metadata comparison.
If you are using MLflow, we would love to chat with you and share our progress on the project for feedback.
https://aimstack.io/aimlflow
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In this digital era, several law enforcement agencies across the globe are leveraging artificial intelligence (AI) to resolve more criminal cases in a very short time equipped with AI algorithms developed to identify, locate and arrest the real or potential criminals faster than ever. Read more
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https://blog.r2c.io/ar-vr-the-next-frontier-in-banking-and-financial-services/
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Recently, I am stumbling frequently across this book: https://www.oreilly.com/library/view/analytical-skills-for/9781492060932/. I am thinking about buying it. Are here some people who own this book and can give some recommendations?
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We present an end-to-end deep view aggregation method for 3D semantic segmentation from images and point clouds. We reach SOTA on S3DIS and KITTI360 without requiring point cloud colorization, meshing, or depth sensors: just point clouds, images, and their poses.
preprint | code | paperwithcode
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Hey everyone!
As the title says, I recently wrote a technical article where I built an uplift model to increase marketing ROIs by targeting the right group of people (the persuadables).
Here's the link: https://towardsdatascience.com/targeting-the-right-group-with-uplift-modelling-5682de2dff8b
Curious to know if anyone has successfully used uplift modeling in their industry or field? And let me know what you think. Any feedback is greatly appreciated!
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Paper: https://arxiv.org/abs/2205.05061
Videos: https://www.youtube.com/watch?v=8k9FNxIU0KQ
Github: Coming soon
Playlist: https://www.youtube.com/watch?v=WXMHJszkz6M&list=PL2KGNY2Ei3ix7Vr_vA-ZgCyVfOCfhbX0C
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This blog post is co-written by Rudra Hota and Esaias Pech from Continental AG. Many drivers have had the experience of trying to adjust temperature settings in their vehicle while attempting to keep their eyes on the road. Whether the previous driver preferred a warmer cabin temperature, or you’re now wearing warmer clothing, or the […]
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In Part 6.0 of the Transfer Learning series we have discussed about Mobilenet pre-trained model in depth so in this series we will…
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There’s an underlying theme to this week’s articles, which is a curious occurrence given that so much of our content is user-driven. That theme is the Value of Data. There is a tendency when looking at data in its various incarnations to view all data as somehow being valuable. Realistically, without rolling up sleeves and… Read More »DSC Weekly Newsletter 10 May 2022: Data Meshes, Digital Twins, and Knowledge Graphs
The post DSC Weekly Newsletter 10 May 2022: Data Meshes, Digital Twins, and Knowledge Graphs appeared first on Data Science Central.
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With continuous advancements in Artificial Intelligence (AI), the manufacturers are spearheading to apply of it in their manufacturing processes to boost product quality, operational efficiency, workforce safety, and many more. Read more
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Hi everyone,
Upon reading [PIFuHD: Multi-Level pixel-aligned implicit function for high-resolution 3d human digitization](https://arxiv.org/abs/2004.00452), I recognized that they used [BUFF dataset](https://buff.is.tue.mpg.de/index.html) which is open only for academic purpose.
To access this data, I have to register academic e-mail and send some declaration papers....
I just want to know how big this dataset is, maybe more than TB..?
Does anyone know how big this is?
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Hi everyone
Just wanted to share a really small (micro) python command line utility that interfaces with `pycoco` library that allows you to generate tiff masks from the coco image dataset for training with semantic segmentation (i.e. UNet) where you can also filter by categories. Found it useful if you want to extract specific images from the Coco dataset for your own semantic segmentation project. Tiff images contain pixel values already representing class labels 0, 1, 2 etc... Hope someone else finds it useful!
https://github.com/ralampay/pycocosegmentor
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A thoughtful approach to AI ethics is becoming increasingly important for all organizations deriving value from AI. We hope that by providing an overview of the top toolkits and resources that exist – starting with Fairness and Robustness – will help more companies adopt AI responsibly, with ethical principles at the core. https://www.borealisai.com/en/blog/industry-analysis-ai-fairness-toolkits-landscape/
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The Nodding Pigeon library provides a pre-trained model and a simple inference API for detecting head gestures in short videos. Under the hood, it uses Google MediaPipe for collecting the landmark features.
For ML practitioners, this project is also an example of using generative data from a small base-dataset for model training.
Please take a look! :)
https://github.com/bhky/nodding-pigeon
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Here is a video explaining the model architecture of the DALLE-2 architecture: https://youtu.be/Z8E3LxqE49M
The paper title is, "Hierarchical Text-Conditional Image Generation with CLIP Latents" and the arxiv link to the paper is here: https://arxiv.org/abs/2204.06125
Official website is here: https://openai.com/dall-e-2
Hope its useful.
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Hi guys, I recently created a new repo on Github, it contains a lists of RL agents to solve discrete action space problems like classic control and Atari games. It includes the most recent algorithms from DeepMind like Never Give Up and Agent57 (also not fully tested on Atari games yet because lack of hardware resources). Hope you will find it helpful.
https://github.com/michaelnny/deep_rl_zoo
The post was originally posted on r/reinforcementlearning a few days ago, but I though re-posting here might reach more people, have a good day!
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After talking to many customers recently at Dell Technologies World, I am very, very (very!) concerned how many organizations are putting their Data Strategy success into the hands of Data Meshes. Sorry, but I think the way that IT organizations are thinking about a Data Mesh is fool’s gold. I think the data mesh (along… Read More »Is Data Mesh Fool’s Gold? Creating a Business-centric Data Strategy
The post Is Data Mesh Fool’s Gold? Creating a Business-centric Data Strategy appeared first on Data Science Central.
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Subtitle creation on video content poses challenges no matter how big or small the organization. To address those challenges, Amazon Transcribe has a helpful feature that enables subtitle creation directly within the service. There is no machine learning (ML) or code writing required to get started. This post walks you through setting up a no-code […]
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Human intuition is usually good at dealing with concepts like averages or mean-values, whereas it often performs poorly when it comes to…
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The future of content creation is in AI. This week In the NVIDIA Studio, discover how AI-assisted painting is bringing a new level of inspiration to the next generation of artists.
The post Creator Karen X. Cheng Brings Keen AI for Design ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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We benchmarked on more than 100K series and show that you can improve MAPE forecast accuracy by 17% with 37x less computational time using Nixtlas StatsForecast. That's the difference between paying $10 or $296 on AWS.
It’s time to overcome the false prophets.
Check Nixtla's FB-Prophet adapter: https://github.com/Nixtla/statsforecast/tree/main/experiments/arima_prophet_adapter
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The two lines you need
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The daily volume of third-party and user-generated content (UGC) across industries is increasing exponentially. Startups, social media, gaming, and other industries must ensure their customers are protected, while keeping operational costs down. Businesses in the broadcasting and media industries often find it difficult to efficiently add ratings to content pieces and formats to comply with […]
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User-generated content (UGC) grows exponentially, as well as the requirements and the cost to keep content and online communities safe and compliant. Modern web and mobile platforms fuel businesses and drive user engagement through social features, from startups to large organizations. Online community members expect safe and inclusive experiences where they can freely consume and […]
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Working as an aerospace engineer in Malaysia, Chee How Lim dreamed of building a startup that could really take off. Today his company, Tapway, is riding a wave of computer vision and AI adoption in Southeast Asia. A call for help in 2019 with video analytics led to the Kuala Lumpur-based company’s biggest project to Read article >
The post More Freedom on the Freeway: AI Lifts Malaysia’s Toll Barriers appeared first on NVIDIA Blog.
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Have a question about numerical differential equations? Odds are this CSAIL research affiliate has already addressed it.
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In the previous post, we discussed the various definitions of digital twins and we see that there are no shortage of them! But here is a question we discussed in class Should the definition of Digital twins include simulation of complex systems? In my opinion, simulation is the raison d’être for digital twins let me explain (some of the ideas… Read More »Should the definition of Digital twins include simulation of complex systems?
The post Should the definition of Digital twins include simulation of complex systems? appeared first on Data Science Central.
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Deep learning, natural language processing, data analytics, and big-data mining are fields of Artificial Intelligence (AI), and many companies are looking for professionals in these fields. A professional degree in AI from a reputed university will help you get started in this industry. Read more
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Cognitive biases are an essential and serious issue. Especially for people who deal with data (algorithms).
I've compiled a list (pdf/EPUB) of over 160 biases (mainly from Wikipedia). Maybe this is useful for some.
These biases affect belief formation, reasoning processes, business & economic decisions, and human behavior in general.
Let's learn more about our human biases to make less biased conclusions in the future.
The PDF/EPUB can be downloaded for free on leanpub: Cognitive Biases: A Brief Overview of Over 160 Cognitive Biases
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Federated learning is a machine learning technique that trains a model over several dispersed nodes or hosts, as the name suggests. Each node utilizes its own training data. If the model parameters are shared between nodes rather than the raw data, the data can be kept private.
Due to privacy concerns, obtaining training data to design and evolve machine learning models is increasingly being questioned, and federated learning can help alleviate some of these issues.
The Chinese e-commerce behemoth, Alibaba, has created a federated learning platform that allows machine learning algorithms to be constructed without sharing training data.
Continue Reading
Github: https://github.com/alibaba/FederatedScope
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I'm reading the paper [On the uncertainty principle of neural networks](https://arxiv.org/abs/2205.01493), and i'm doubting one inequality.
In the middle of the equation (7) and (8), there's a part that states
If we use the property ~~~
https://preview.redd.it/tz6k5htt0cy81.png?width=774&format=png&auto=webp&s=99fc8b5701e00fdf253e2f52eb2fde240772fe5e
Is the yellow inequality true? I think the inequality should be opposite, due to arithmetic–geometric mean inequality.
Thks.
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Hello community,
I am a bit baffled scikit-learn does not support this.
I am looking for a good python library that enables fitting a decision tree regressor on both numerical and categorical features (non-binary tree).
Could you point me to one if you know any, please? Thanks!
(PS: This is for visualization and interpretability, so things like catboost won't do)
EDIT: I think I may have found what I was looking for: chefboost (PS: unfortunately it is a bit simplistic and does not support pruning atm)
Also xMattC3 pointed this ongoing PR for scikit learn: NOCATS
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The guide introduces Terraform Provider Iterative (TPI) - an open-source tool extending the functionality of Terraform. The tool enables full lifecycle management of computing resources and is designed specifically for ML pipelines: Machine Learning Workloads with Terraform Provider Iterative
It was designed for machine learning (ML/AI) teams and optimizes CPU/GPU expenses. TPI unifies auto-scaling groups for all the major cloud providers: AWS, Azure, GCP and Kubernetes.
Spot instances auto-recovery (if an instance was evicted/terminated) with data and checkpoint synchronization
Auto-terminate instances when ML training is finished - you won't forget to terminate your expensive GPU instance for a week :)
Using Terraform commands and config (HCL)
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Mοst mοdеrn-day smartphοnеs with grеat camеras makе it rеally еasy fοr anyοnе and еvеryοnе tο click gοοd phοtοs. But what mοst pеοplе dοn’t rеalizе is that with еach phοtοgraph, thеy’rе alsο capturing an lοt οf pеrsοnal infοrmatiοn which, whеn thеy sharе that phοtο οn sοcialmеdia, bеcοmеs availablе tο a much widеrsеt οf pеοplе οn thе… Read More »Understanding EXIF Data and How to View It on Android Phones
The post Understanding EXIF Data and How to View It on Android Phones appeared first on Data Science Central.
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Last few years ago, the industrial revolution is the most popular evolution ever faced by the industrial sector. It encompasses all the latest technology trends which affecting industries over the world. Autonomous cars, smart connected devices, sensors, computer chips, and many other technologies represented this transformation. This happens due to the manufacturing industry has been… Read More »AI In Manufacturing: Know How Latest Intelligence Reshaping the Industries with Speed and Accuracy
The post AI In Manufacturing: Know How Latest Intelligence Reshaping the Industries with Speed and Accuracy appeared first on Data Science Central.
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A rise in netizens and social media platforms, coupled with the growth of the mobile internet, has resulted in a jump in the creation and consumption of User Generated Content (UGC). Social media platforms, in all honesty, have become a major channel for disseminating, circulating, and exchanging information to billions of people on the internet… Read More »Social Media, Cyber Bullying, and Need For Content Moderation
The post Social Media, Cyber Bullying, and Need For Content Moderation appeared first on Data Science Central.
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Data management (DM) discussions can be frustrating because both those feeling the pain and the consultants who try to help them are–90+ percent of the time, it seems–still using the same old ways. Those ways only go so far, and won’t go any farther. That’s because those who reinforce the old ways assume that what… Read More »The long game: Desiloed systems and feedback loops by design (I of II)
The post The long game: Desiloed systems and feedback loops by design (I of II) appeared first on Data Science Central.
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Warning: This is going to get heavy into Turtle code, but I think there’s enough here for it to be worth reading if you are involved in knowledge graph work. I’ve been working with knowledge graphs a lot lately, and a conversation that I had with a few other ontologists has been resonating in my… Read More »The Graph of Thrones: the Now Graph and Eternal Graph in RDF-Star Modeling
The post The Graph of Thrones: the Now Graph and Eternal Graph in RDF-Star Modeling appeared first on Data Science Central.
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In my previous post, I defined an experimentation program (ExPr) as the mechanism by which a company uses randomized controlled experiments to generate positive business results. An ExPr is composed of the people, processes, and infrastructure for running experiments at… Read More
The post Driving Experimentation Forward through a Working Group (Experimentation Program Series: Guide 03) appeared first on ML in Production.
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In my previous post, I defined an experimentation program (ExPr) as the mechanism by which a company uses randomized controlled experiments to generate positive business results. An ExPr is composed of the people, processes, and infrastructure for running experiments at… Read More
The post Driving Experimentation Forward through a Working Group (Experimentation Program Series: Guide 03) appeared first on ML in Production.
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Reinforcement learning has immediate applications in industrial robotics and other control oriented tasks. Are there any interesting real-world applications of RL that is less obvious than robotics or trading? I saw one application in the crypto space and am curious about the other different possible applications (can be in any sector) of RL
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I created this Streamlit website for Jina AI's awesome DALL·E Flow project.
What do you think?
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Amazon SageMaker Data Wrangler reduces the time to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio. Data Wrangler can simplify your data preparation and feature engineering processes and help you with data selection, cleaning, exploration, and visualization. Data Wrangler has over 300 built-in transforms written in PySpark, […]
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Approximately 7,000 languages are in use today. Despite attempts in the late 19th century to invent constructed languages such as Volapük or Esperanto, there is no sign of unification. People still choose to create new languages (think about your favorite movie character who speaks Klingon, Dothraki, or Elvish). Today, natural language processing (NLP) examples are […]
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In recent years, natural language understanding (NLU) has increasingly found business value, fueled by model improvements as well as the scalability and cost-efficiency of cloud-based infrastructure. Specifically, the Transformer deep learning architecture, often implemented in the form of BERT models, has been highly successful, but training, fine-tuning, and optimizing these models has proven to be […]
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Organizations today have been using some form of document management for years, whether on paper, computer, or online. While we at Bentech…
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